Dynamics of propagation of premature impulses in structurally remodeled infarcted myocardium: a computational analysis

Initiation of cardiac arrhythmias typically follows one or more premature impulses either occurring spontaneously or applied externally. In this study, we characterize the dynamics of propagation of single (S2) and double premature impulses (S3), and the mechanisms of block of premature impulses at structural heterogeneities caused by remodeling of gap junctional conductance (Gj) in infarcted myocardium. Using a sub-cellular computer model of infarcted tissue, we found that |INa,max|, prematurity (coupling interval with the previous impulse), and conduction velocity (CV) of premature impulses change dynamically as they propagate away from the site of initiation. There are fundamental differences between the dynamics of propagation of S2 and S3 premature impulses: for S2 impulses |INa,max| recovers fast, prematurity decreases and CV increases as propagation proceeds; for S3 impulses low values of |INa,max| persist, prematurity could increase, and CV could decrease as impulses propagate away from the site of initiation. As a consequence it is more likely that S3 impulses block at sites of structural heterogeneities causing source/sink mismatch than S2 impulses block. Whether premature impulses block at Gj heterogeneities or not is also determined by the values of Gj (and the space constant λ) in the regions proximal and distal to the heterogeneity: when λ in the direction of propagation increases \u3e40%, premature impulses could block. The maximum slope of CV restitution curves for S2 impulses is larger than for S3 impulses. IN CONCLUSION: (1) The dynamics of propagation of premature impulses make more likely that S3 impulses block at sites of structural heterogeneities than S2 impulses block; (2) Structural heterogeneities causing an increase in λ (or CV) of \u3e40% could result in block of premature impulses; (3) A decrease in the maximum slope of CV restitution curves of propagating premature impulses is indicative of an increased potential for block at structural heterogeneities

Multichannel modulation of depolarizing and repolarizing ion currents increases the positive rate-dependent action potential prolongation

Prolongation of the action potential duration (APD) could prevent reentrant arrhythmias if prolongation occurs at the fast excitation rates of tachycardia with minimal prolongation at slow excitation rates (i.e., if prolongation is positive rate-dependent). APD prolongation by current anti-arrhythmic agents is either reverse (larger APD prolongation at slow rates than at fast rates) or neutral (similar APD prolongation at slow and fast rates), which may not result in an effective anti-arrhythmic action. In this report we show that, in computer models of the human ventricular action potential, the combined modulation of both depolarizing and repolarizing ion currents results in a stronger positive rate-dependent APD prolongation than modulation of repolarizing potassium currents. A robust positive rate-dependent APD prolongation correlates with an acceleration of phase 2 repolarization and a deceleration of phase 3 repolarization, which leads to a triangulation of the action potential. A positive rate-dependent APD prolongation decreases the repolarization reserve with respect to control, which can be managed by interventions that prolong APD at fast excitation rates and shorten APD at slow excitation rates. For both computer models of the action potential, ICaL and IK1 are the most important ion currents to achieve a positive rate-dependent APD prolongation. In conclusion, multichannel modulation of depolarizing and repolarizing ion currents, with ion channel activators and blockers, results in a robust APD prolongation at fast excitation rates, which should be anti-arrhythmic, while minimizing APD prolongation at slow heart rates, which should reduce pro-arrhythmic risks

Transition from Concepts to Practical Skills in Computer Programming Courses: Factor and Cluster Analysis

Computer programming courses are gateway courses with low passing grades, which may result in student attrition and transfers out of engineering and computer science degrees. Barriers to success include a good understanding of programming concepts and the ability to apply those concepts to write viable computer programs. In this paper, we analyze the determinants of the transition from concepts to skills in computer programming courses using factor and cluster analysis. The purpose of this study is to answer the following questions related to computer programming teaching and learning: 1) Which are the correlations and interdependencies in student understanding of different computer programming concepts?; 2) Which are the cognitive challenges that students find when learning programming concepts?; 3) How the understanding of different programming concepts relate to practical skills in computer programming; 4) What determines a successful transition from understanding the concepts to the ability to write viable computer programs? After several computer programming concept assessments in this first Java Programming course, we grouped the students’ performance into seven different categories: assignment operators, repetition structures, selection structures, program design using methods, arrays, classes and Java syntax. Factor analysis identified two factors (components) grouping the interdependencies and correlations between programming concept categories. The first component correlated with the repetition and selection categories, and could be referred to as the “algorithmic” component. The second component correlated with the methods, arrays and assignment categories, and could be referred as the “structural” component. Student performance in conceptual categories related to the “algorithmic” factor was significantly better than in conceptual categories related to the “structural” factor. Cluster analysis showed that student performance in the “structural” conceptual component is predictive of the student’s ability to solve practical computer programming problems. We conclude that a strong emphasis in the structural components of computer programming (i.e. program design using methods, use of the assignment operator, and use of data structures like arrays) is necessary for a successful transition from concepts to skills in computer programming courses

Positive Rate-Dependent Action Potential Prolongation by Modulating Potassium Ion Channels

Pharmacological agents that prolong action potential duration (APD) to a larger extent at slow rates than at the fast excitation rates typical of ventricular tachycardia exhibit reverse rate dependence. Reverse rate dependence has been linked to the lack of efficacy of class III agents at preventing arrhythmias because the doses required to have an anti-arrhythmic effect at fast rates may have pro-arrhythmic effects at slow rates due to an excessive APD prolongation. In this report we show that, in computer models of the ventricular action potential, APD prolongation by accelerating phase 2 repolarization (by increasing IKs) and decelerating phase 3 repolarization (by blocking IKr and IK1) results in a robust positive rate dependence (i.e., larger APD prolongation at fast rates than at slow rates). In contrast, APD prolongation by blocking a specific potassium channel type results in reverse rate dependence or a moderate positive rate dependence. Interventions that result in a strong positive rate dependence tend to decrease the repolarization reserve because they require substantial IK1 block. However, limiting IK1 block to ~50% results in a strong positive rate dependence with moderate decrease in repolarization reserve. In conclusion, the use of a combination of IKs activators and IKr and IK1 blockers could result in APD prolongation that potentially maximizes anti-arrhythmic effects (by maximizing APD prolongation at fast excitation rates) and minimizes pro-arrhythmic effects (by minimizing APD prolongation at slow excitation rates)

Quantifying Student Progress Through Bloom\u27s Taxonomy Cognitive Categories in Computer Programming Courses

Quantifying Student Progress through Bloom’s Taxonomy Cognitive Categories in Computer Programming Courses Computer programming courses are gateway courses with low passing grades, which may result in student attrition and transfers out of engineering and computer science degrees. Progress in student learning can be conceptualized by the different cognitive levels or categories described in Bloom’s taxonomy, which, from the lowest to the highest order processes, include: knowledge, comprehension, application, analysis, evaluation, and synthesis. The purpose of this study is to gain insight into how students transfer their conceptual knowledge and comprehension of computer programming concepts (knowledge and comprehension categories in Bloom’s taxonomy) into their ability to write computer programs (application category in Bloom’s taxonomy), using Bloom’s taxonomy as a framework. The following research questions were addressed in this study: 1) Is adequate performance in conceptual understanding sufficient for a student to write viable computer programs? 2) How big is the gap between conceptual understanding of programming concepts and the ability to apply those concepts to write viable computer programs? 3) Are some concepts more important than others in determining students’ ability to write viable programs? A total of 62 students who took a first computer programming course using Java participated in this study from spring 2013 to spring 2014. Novice computer programming students face two barriers in their progress to become proficient programmers: a good understanding of programming concepts (first two categories in Bloom’s taxonomy) and the ability to apply those concepts (third category in Bloom’s taxonomy) to write viable computer programs. About 35%of students had an acceptable performance in both conceptual understanding of programming concepts and ability to write viable programs. About 44% of students had an inadequate performance in both concepts and programming skills. 16% of students had an adequate understanding of computer concepts but were unable to transfer that understanding into writing viable computer programs. Finally, 5% of students were able to produce viable computer programs without an adequate conceptual understanding. Of the students who had adequate understanding of computer concepts, 69% were able to write viable computer programs. Linear regression modeling suggests that conceptual understanding is a good predictor (R squared =74%) of the ability to apply that knowledge to write computer programs. Multiple regression analysis shows that some concepts are better predictors of programming skills than others: performance in conceptual assessments on Java syntax, classes and repetition structures are better predictors of the ability to write viable programs than performance in conceptual assessments on assignment operators, program design using methods and arrays. In conclusion: 1) Many students (44%) do not reach and adequate level of conceptual knowledge and understanding and cannot write viable computer programs; 2) Some students (16%) cannot transfer conceptual knowledge and understanding into viable computer programs; 3) Regression analysis between student performance in programming concepts and students’ ability to write viable computer programs can be used to align better the concepts taught and the expected student skills, and to facilitate student progress through the different cognitive levels in Bloom’s taxonomy

Integrating Creative Writing and Computational Thinking to Develop Interdisciplinary Connections

A typical college curriculum does not make it easy for students to establish connections between required general education courses and courses in their majors. Intentional linking of courses from different disciplines using interdisciplinary pedagogical strategies allows students to make those connections while developing the interdisciplinary skills which will benefit their college and post-college careers. In addition to communication, critical thinking and reasoning, and collaborative skills, it has been recently argued that computational thinking (i.e., the application of computing concepts and methods to solve problems) should also be a part of a twenty-first century liberal education for a broad range of college students, including those not majoring in computing. Computational thinking concepts and skills can help students frame problems in a variety of fields and disciplines (not just STEM disciplines) using novel strategies, and, in so doing, to become better problem solvers in their professions. At our institution, many students not majoring in computing (or a STEM discipline) take a first-year problem-solving with computer programming course (PS), which is designed for Computer Science majors, to satisfy the computer literacy/fluency requirement in their degree or to learn computational thinking concepts and skills. However, since PS is a gateway course for Computer Science majors, it is even more challenging for non-majors, resulting in high non-passing and withdrawal rates. To integrate computational thinking in required liberal arts courses, we created a general education interdisciplinary course, Programming Narratives: Computer Animated Storytelling, aimed at non-computer majors, which emphasizes creative writing and computational thinking. In this interdisciplinary course, students learn the structure of narrative, concepts of problem solving, and the logic of computer programming languages as they develop a narrative-driven video game prototype. This process helps students achieve the college-wide learning goal of making meaningful and multiple connections among the liberal arts majors, as well as between the liberal arts and the areas of study leading to a major or profession. Our findings suggest that the learning objectives and the pedagogical approaches used in the course are adequate for a broad range of non-computer majors. Performance on writing and computing assessments as well as final grades (75% of students obtained a grade of C or better) indicated that a vast majority of students successfully achieved the learning objectives. These results were consistent with student perceptions as reflected in an end-of-course survey. There is also evidence that students satisfactorily integrated creative writing and computer programming to develop their video game prototypes, making in-depth interdisciplinary connections along the way. We believe that this intentional emphasis on connections between disciplines develops the interdisciplinary skills and perspectives which are important for graduation, and it lays the groundwork for interdisciplinary thinking in the workplace

Using Student-Developed Narratives to Improve Learning and Engagement in Computer Problem-Solving Courses

In our Computer Systems major, we require all students to take a problem-solving course (PS) to prepare them for subsequent courses in computer programming. As part of the PS course, students learn basic procedural programming concepts such as input, sequencing, selection (if/else), repetition (for and while loops), and output, using flowchart interpreters like Visual Logic (www.visuallogic.org). When trying to solve flowcharting problems, students have difficulty translating word problems into computer algorithms. Moreover, most problems proposed to students are closely related to mathematics and accounting, and our students are not well prepared in mathematics. Partly for this reason, students are often not interested or engaged by the problems proposed to them in the flowcharting component of the course. Researchers have shown that understanding and engaging the problem domain to be solved by implementing a computer program should be a prerequisite for writing the computer program itself. Therefore, the students’ inability to create a mental model of a given problem domain hinders their ability to develop problem-solving skills and write computer programs. The goal of our project was to create problem domains that students could understand, relate to, and be engaged with, so they can be used as the contexts to develop problem-solving and procedural programming skills in the flowcharting component of the PS course. Our approach is based on the premise that students themselves know better which problems are relevant to them, which problems they can relate to and understand. We selected a group of five students majoring in Computer Systems who had passed the PS course in the last three years and gave them the task of developing stories that could be used as context to solve flowcharting problems. These five students completed a section of the PS course that was linked to an English Composition course in a learning community (LC), so, in addition to understanding well the course in which the stories were going to be used, they had considerable narrative and writing skills. The students themselves suggested which flowcharting assignments could be fostered by the stories. The stories were developed iteratively, following a combination of individual writing, group discussion, and faculty suggestions, to further improve the versions of the stories. Students were provided with a small stipend. The use of student-developed narratives affected performance in different flowcharting structures differently. Overall the data suggests that the use of case studies was beneficial for increasing performance in selection assessments, modestly beneficial for repetition assessments and of no benefit for sequence assessments. Despite the learning benefit, a majority of students and instructors were resistant to use case-studies in this course. A majority of students thought that reading stories does not belong in a problem-solving/computer programming class, which indicates that students tend to compartmentalize learning